Edge Weight

Edge weight, representing the strength or cost of connections in graphs, is crucial for numerous applications, from network analysis to image segmentation. Current research focuses on developing efficient algorithms for learning and predicting edge weights, particularly within graph neural networks (GNNs) and other machine learning models, often incorporating techniques like linear programming, conformal prediction, and graph propagation. These advancements improve the accuracy and efficiency of graph-based analyses, impacting diverse fields including transportation modeling, social network analysis, and biological network inference. Furthermore, research addresses challenges like handling uncertainty in edge weights and mitigating the effects of adversarial attacks on graph structures.